Model comparison
GPT-5 Mini vs Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 41.7 on the Noometry Index. GPT-5 Mini costs 2.3× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Summary
- They share 18 benchmarks with published results for both. GPT-5 Mini scores higher in 3 categories and Grok 4.20 Multi-Agent in 6 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 42.4 to 24.1.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $1.25 / $2.50 for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5 Mini | Grok 4.20 Multi-Agent | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 41.7 | 45.5 |
| Released | 2025-08-07 | 2026-03-09 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1M |
| Max output | 128K | 30K |
| Input $ / M tokens | $0.25 | $1.25 |
| Output $ / M tokens | $2 | $2.50 |
| Results tracked | 60 | 20 |
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Category by category
Coding Grok 4.20 Multi-Agent leads
GPT-5 Mini: 40.0 (#134), Grok 4.20 Multi-Agent: 42.4 (#91)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Coding | 1406 | 1458 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| WeirdML | 52.7% | — |
| ALE-Bench | 799.77 | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use Not comparable
GPT-5 Mini: 31.0 (#64), Grok 4.20 Multi-Agent: —
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| Terminal-Bench | 34.8% | — |
| Berkeley Function Calling Leaderboard | 55.5% | — |
| LMArena Search | — | 1204 |
| Vending-Bench 2 | -31.18 | — |
Reasoning Grok 4.20 Multi-Agent leads
GPT-5 Mini: 24.1 (#153), Grok 4.20 Multi-Agent: 42.4 (#44)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Hard Prompts | 1380 | 1448 |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| NYT Connections (extended) | — | 89.6% |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| Mystery Game Puzzles | 10% | — |
| DTBench | 80.5% | — |
| LMCA | 34.2% | — |
| Epoch Capabilities Index | 145.51 | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.3 (#59), Grok 4.20 Multi-Agent: 36.6 (#106)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Math | 1377 | 1441 |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.5 (#80), Grok 4.20 Multi-Agent: 39.7 (#116)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Expert | 1377 | 1445 |
| GPQA Diamond | 75% | — |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| Vectara Hallucination Rate | 12.9% | — |
| GPQA (HELM) | 75.6% | — |
Multimodal Grok 4.20 Multi-Agent leads
GPT-5 Mini: 35.0 (#80), Grok 4.20 Multi-Agent: 39.8 (#44)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Vision | 1202 | 1259 |
| VPCT | 40.2% | — |
Multilingual Grok 4.20 Multi-Agent leads
GPT-5 Mini: 49.1 (#131), Grok 4.20 Multi-Agent: 54.6 (#40)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Non-English | 1363 | 1440 |
| LMArena Chinese | 1387 | 1475 |
| LMArena French | 1385 | 1467 |
| LMArena German | 1366 | 1456 |
| LMArena Japanese | 1342 | 1405 |
| LMArena Korean | 1306 | 1416 |
| LMArena Russian | 1362 | 1457 |
| LMArena Spanish | 1355 | 1447 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.4 (#43), Grok 4.20 Multi-Agent: 75.0 (#80)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Instruction Following | 1357 | 1420 |
| IFEval | 92.7% | — |
Long Context Grok 4.20 Multi-Agent leads
GPT-5 Mini: 41.9 (#127), Grok 4.20 Multi-Agent: 43.7 (#84)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Longer Query | 1355 | 1430 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference Grok 4.20 Multi-Agent leads
GPT-5 Mini: 55.4 (#143), Grok 4.20 Multi-Agent: 64.1 (#57)
| Benchmark | GPT-5 Mini | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Text | 1373 | 1450 |
| LMArena Creative Writing | 1325 | 1435 |
| LMArena Multi-Turn | 1363 | 1451 |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
Frequently asked questions
Is GPT-5 Mini better than Grok 4.20 Multi-Agent?
Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 41.7 on the Noometry Index. GPT-5 Mini costs 2.3× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Mini or Grok 4.20 Multi-Agent?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.
Is GPT-5 Mini or Grok 4.20 Multi-Agent better for coding?
Grok 4.20 Multi-Agent scores higher on coding benchmarks: 42.4 versus 40.0 in the Noometry coding category.
Which has the bigger context window?
Grok 4.20 Multi-Agent does, with 1M tokens against 400K.
How many benchmarks do GPT-5 Mini and Grok 4.20 Multi-Agent share?
18 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Grok 4.20 Multi-Agent has 20.